Trade Liberalization and Regional Dynamics

Transcription

1 Trade Liberalization and Regional Dynamics Rafael Dix-Carneiro 1 Brian K. Kovak 2 1 Duke University NBER and BREAD 2 Carnegie Mellon University - Heinz College NBER and IZA Conference on Inequality, Globalization and Macroeconomics University of Southern California April 29, 2017

2 Regional Effects of Trade Freer trade generates overall economic gains but is also likely to create winners and losers (Stolper and Samuelson 1941) 60 years of work examining trade s effects on workers with different skills or in different industries Starting late 2000s: trade has vastly different effects on workers in different local labor markets Effects determined by regional industry mix Trade s costs and benefits are unevenly distributed geographically, not just across industries or skills

3 Slow Adjustment to Trade No / Perfect mobility in trade theory: short- / long-run models Policy makers: what happens in the short- to medium-run? How do we get to the long run? Recent work on transitional dynamics of trade liberalization Kambourov (2009), Artuc, Chaudhuri and Mclaren (2010), Cosar (2013), Dix-Carneiro (2014), Caliendo, Dvorkin and Parro (2015), Helpman and Itskhoki (2015), Traiberman (2016), Bellon (2016), and many others. Structural estimation/calibration followed by simulation We document observed dynamics of adjustment following real-world trade liberalization

4 Contributions Literature finding substantially different effects of trade shocks across local labor markets Topalova (2007), Kovak (2013), Autor, Dorn and Hanson (2013), and many others Effects estimated over windows of 7/10 years. We estimate the evolution of the local effects of the early 1990s Brazilian trade liberalization Discrete liberalization shock empirical labor market dynamics Compare existing medium-run estimates to short- and long-run

5 Brazilian Trade Liberalization Tariff Changes Change in ln(1+tariff), Agriculture Metals Apparel Food Processing Wood, Furniture, Peat Textiles Nonmetallic Mineral Manuf Paper, Publishing, Printing Mineral Mining Footwear, Leather Chemicals Auto, Transport, Vehicles Electric, Electronic Equip. Machinery, Equipment Plastics Other Manuf. Pharma., Perfumes, Detergents Petroleum Refining Rubber Petroleum, Gas, Coal

6 Empirical Approach Follow Kovak (2013): trade-induced labor demand shocks Regional Tariff Reductions RTR r = i β ri d ln(1 + τ i ) λ 1 ri ϕ i β ri j λ rj 1 ϕ j i: industry τ i : tariff rate in industry i d: long difference from λ ri : share of regional employment in industry i ϕ i : 1 minus labor share of value added

7 Regional Tariff Reductions Manaus Belém Fortaleza Recife Salvador 8% to 15% 4% to 8% 3% to 4% 1% to 3% -1% to 1% Brasília Curitiba São Paulo Belo Horizonte Porto Alegre gap 0.10

8 Data Description Administrative Data: RAIS Census of Brazilian formal labor market Establishment-level information geographic location (municipality), industry, employment Worker-level information gender, age, education (9 categories), earnings Years:

9 Empirical Approach Compare evolution of outcomes across regions facing larger vs. smaller tariff cuts y rt y r,1991 = θ t RTR r + α st + γ t (y r,1990 y r,1986 ) + ɛ rt, y rt: value of region r outcome in post-lib year t α st: state fixed effects θ t: effect of liberalization on change in outcome by year t. RAIS analysis also includes pre-liberalization outcome trend control, (y r,1990 y r,1986 ) Outcomes formal employment formal earnings premium: ˆµ rt from ln(earn jri ) = µ rt + φ it + X jt β t + e jrit

10 Results Employment 1.5 Pre- liberaliza6on (chg. from 1986) Liberaliza6on Post- liberaliza6on (chg. from 1991)

11 Results Earnings 1.0 Pre- liberaliza6on (chg. from 1986) Liberaliza6on Post- liberaliza6on (chg. from 1991)

12 Dynamics in Labor Demand r = 1,..., R regions and i = 1,..., I industries CRS Cobb-Douglas production (shares may vary by i) Y ri = A ri L 1 ϕ i ri ( ) T ζ i ri K 1 ζ ϕi i ri where ϕ i, ζ i (0, 1) L r labor, perfectly mobile between industries within regions T ri fixed factor (e.g., resources), specific to region and industry K ri capital, specific to region and industry, may change through depreciation and investment A ri region-industry Cobb-Douglas productivity shifter Competitive markets. Exogenous output price P i Examine effects of changing vector of output prices (liberalization)

13 Dynamics in Labor Demand Factor market clearing, zero profits, and cost minimization imply equilibrium relation ŵ r = β ri ˆP i + ( β ri  ri δ r ˆL r ) λ ri (1 ζ i ) ˆK ri i i i where β ri λ ri ϕ i j λ rj 1 and δ r ϕ j 1 1 k λ rk 1 ϕ k.

14 Dynamics in Labor Demand Evidence for Slow Capital Adjustment Number of formal establishments proxy for regional capital stock 2 Pre- liberaliza5on (chg. from 1986) Liberaliza5on Post- liberaliza5on (chg. from 1991) 1 Establishments Pretrend Estab. Size Pretrend Establishment Size Establishments - 5

15 Dynamics in Labor Demand Evidence for Slow Capital Adjustment Immediate investment response, slow depreciation response 5 Pre- liberaliza5on (chg. from 1986) Liberaliza5on Post- liberaliza5on (chg. from 1991) 4 Exit Entry Pretrend Exit Pretrend Entry - 3

16 Dynamics in Labor Demand Evidence for Slow Capital Adjustment Immediate investment response, slow depreciation response 5 Pre- liberaliza5on (chg. from 1986) Liberaliza5on Post- liberaliza5on (chg. from 1991) 4 3 Job Destruc)on 2 1 Job Crea)on Pretrend Job Destruc)on Pretrend Job Crea)on - 4 Job creation and destruction defined as in Davis and Haltiwanger (1990)

17 Dynamics in Labor Demand Evidence for Agglomeration Economies Assume in the long run... Constant elasticity agglomeration function (Glaeser Gottlieb 2008, Kline Moretti 2014): Â ri = κˆl r Constant elasticity labor supply: ˆL r = 1 η ŵr Perfectly mobile capital ˆR r = ˆR, r

18 Dynamics in Labor Demand Evidence for Agglomeration Economies Imposing these assumptions yields the following expressions ŵ r = η η[1 ϕ(1 ζ)] κ + ϕζ β ri ˆPi i ϕ(1 ζ)η η[1 ϕ(1 ζ)] κ + ϕζ ˆR ˆL ri = 1 ϕζ ˆP i 1 ϕζ η[1 ϕ(1 ζ)] κ ϕ(1 ζ) β ri ˆPi η[1 ϕ(1 ζ)] κ + ϕζ η[1 ϕ(1 ζ)] κ + ϕζ ˆR i Test for agglomeration economies ˆL ri = γ 0 + γ 1 ˆP i + γ 2 RTR r + ɛ ri γ 2 < 0 κ > 0

19 Dynamics in Labor Demand Evidence for Agglomeration Economies Test for agglomeration economies Change in log Region x Industry Employment: All industries Tradable industries Tradable industries (1) (2) (3) Regional tariff reduction (RTR) *** *** *** (0.631) (0.675) (0.694) Industry price change controls Industry fixed effects (20) Observations 4,648 4,174 4,174 R- squared ˆγ 2 < 0 κ > 0, i.e. agglomeration economies are present

20 Dynamics in Labor Demand Quantification Estimating agglomeration elasticity, κ Estimate 1/η from ˆL r = (1/η)ŵ r, with RTR as IV for ŵ r Estimate κ using non-linear least squares from each of ŵ r = η η[1 ϕ(1 ζ)] κ + ϕζ β ri ˆPi i ϕ(1 ζ)η η[1 ϕ(1 ζ)] κ + ϕζ ˆR ˆL ri = 1 ϕζ ˆP i 1 ϕζ η[1 ϕ(1 ζ)] κ ϕ(1 ζ) β ri ˆPi η[1 ϕ(1 ζ)] κ + ϕζ η[1 ϕ(1 ζ)] κ + ϕζ ˆR i Bootstrap entire procedure to account for correlation between ˆη and ˆκ

21 Dynamics in Labor Demand Quantification Agglomeration elasticity estimates Panel A: Inverse labor supply elasticity (η) 0.363*** (0.060) Panel B: Agglomeration elasticity (κ) (1) (2) (3) Specific factors' share of non-labor inputs (ζ): low (0.152) mid (0.349) high (0.545) Wage-based agglomeration elasticity (κ) 0.042* 0.188*** 0.333*** (0.023) (0.023) (0.025) Employment-based agglomeration elasticity (κ) 0.215*** 0.330*** 0.461*** (0.032) (0.038) (0.043) Estimates within range of prior literature (Melo et al. 2009) Kline and Moretti (2014) find 0.2

22 Summary Contributions Empirically describe the dynamics of labor market transition in response to trade liberalization Dynamic context for existing static regional results Challenge conventional wisdom on equalizing migration Mechanism that qualitatively and quantitatively explains growing effects Finite elasticity of regional labor supply Agglomeration economies Slow adjustment of complementary factors Benefits and costs from trade liberalization unevenly distributed across space for years after liberalization